{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 1400 # 句子 上限1400个词\n",
    "EMBEDDING_DIM = 100 # 100d 词向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集\n",
    "HTTP DATASET CSIC 2010: http://www.isi.csic.es/dataset/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36000 36000 31020\n"
     ]
    }
   ],
   "source": [
    "# 获取数据集\n",
    "# GET 以 Connection + /n + /n 结尾\n",
    "# POST 以 Content-Length + /n + params + /n 结尾\n",
    "\n",
    "def read_data(path):\n",
    "    data = []\n",
    "    labels = []\n",
    "    with open(path) as f:\n",
    "        while True:\n",
    "            line = f.readline()\n",
    "            content = line\n",
    "            if line == '': break \n",
    "            if line[:3] == 'GET':\n",
    "                while True:\n",
    "                    line = f.readline()\n",
    "                    if line[:10] == 'Connection': break\n",
    "                    else: content += line\n",
    "                f.readline() # none\n",
    "                f.readline() # none\n",
    "            elif line[:4] == 'POST':\n",
    "                while True:\n",
    "                    line = f.readline()\n",
    "                    if line[:14] == 'Content-Length': break\n",
    "                    else: content += line\n",
    "                f.readline() # none\n",
    "                content += f.readline()\n",
    "                f.readline() # none\n",
    "            data.append(content)\n",
    "    return data\n",
    "\n",
    "data1 = 'data/normalTrafficTest.txt'\n",
    "data2 = 'data/normalTrafficTraining.txt'\n",
    "data3 = 'data/anomalousTrafficTest.txt'\n",
    "X1 = read_data(data1)\n",
    "X2 = read_data(data2)\n",
    "X3 = read_data(data3)\n",
    "print(len(X1), len(X2), len(X3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103020 103020\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "labels = []\n",
    "data.extend(X1)\n",
    "labels.extend([1] * len(X1))\n",
    "data.extend(X2)\n",
    "labels.extend([1] * len(X2))\n",
    "data.extend(X3)\n",
    "labels.extend([0] * len(X3))\n",
    "print(len(data), len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GET http://localhost:8080/tienda1/index.jsp HTTP/1.1\\nUser-Agent: Mozilla/5.0 (compatible; Konqueror/3.5; Linux) KHTML/3.5.8 (like Gecko)\\nPragma: no-cache\\nCache-control: no-cache\\nAccept: text/xml,application/xml,application/xhtml+xml,text/html;q=0.9,text/plain;q=0.8,image/png,*/*;q=0.5\\nAccept-Encoding: x-gzip, x-deflate, gzip, deflate\\nAccept-Charset: utf-8, utf-8;q=0.5, *;q=0.5\\nAccept-Language: en\\nHost: localhost:8080\\nCookie: JSESSIONID=EA414B3E327DED6875848530C864BD8F\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POST http://localhost:8080/tienda1/publico/anadir.jsp HTTP/1.1\\nUser-Agent: Mozilla/5.0 (compatible; Konqueror/3.5; Linux) KHTML/3.5.8 (like Gecko)\\nPragma: no-cache\\nCache-control: no-cache\\nAccept: text/xml,application/xml,application/xhtml+xml,text/html;q=0.9,text/plain;q=0.8,image/png,*/*;q=0.5\\nAccept-Encoding: x-gzip, x-deflate, gzip, deflate\\nAccept-Charset: utf-8, utf-8;q=0.5, *;q=0.5\\nAccept-Language: en\\nHost: localhost:8080\\nCookie: JSESSIONID=788887A0F479749C4CEEA1E268B4A501\\nContent-Type: application/x-www-form-urlencoded\\nConnection: close\\nid=1&nombre=Jam%F3n+Ib%E9rico&precio=39&cantidad=41&B1=A%F1adir+al+carrito\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 80 unique tokens.\n",
      "Shape of data tensor: (103020, 1400)\n"
     ]
    }
   ],
   "source": [
    "# https://keras-cn-docs.readthedocs.io/zh_CN/latest/blog/word_embedding/\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# tokenizer\n",
    "texts = data\n",
    "tokenizer = Tokenizer(char_level=True)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# sequences\n",
    "sequences = tokenizer.texts_to_sequences(data)\n",
    "\n",
    "# padding\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "print('Shape of data tensor:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "token_path = 'model/tokenizer.pkl'\n",
    "pickle.dump(tokenizer, open(token_path, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# 打乱顺序\n",
    "index = [i for i in range(len(data))]\n",
    "random.shuffle(index)\n",
    "data = np.array(data)[index]\n",
    "labels = np.array(labels)[index]\n",
    "\n",
    "TRAIN_SPLIT = 0.8 # 20% 测试集\n",
    "TRAIN_SIZE = int(len(data) * TRAIN_SPLIT)\n",
    "\n",
    "X_train, X_test = data[0:TRAIN_SIZE], data[TRAIN_SIZE:]\n",
    "Y_train, Y_test = labels[0:TRAIN_SIZE], labels[TRAIN_SIZE:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train len: 82416\n",
      "test len: 20604\n"
     ]
    }
   ],
   "source": [
    "print('train len:', len(X_train))\n",
    "print('test len:', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "G = 1 # GPU 数量\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n",
    "session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True))\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bi-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 1400, 100)         8100      \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               84480     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 101,161\n",
      "Trainable params: 101,031\n",
      "Non-trainable params: 130\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, BatchNormalization\n",
    "from keras.layers import Dense, LSTM\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "\n",
    "QA_EMBED_SIZE = 64\n",
    "DROPOUT_RATE = 0.3\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH))\n",
    "model.add(Bidirectional(LSTM(QA_EMBED_SIZE, return_sequences=False, dropout=DROPOUT_RATE, recurrent_dropout=DROPOUT_RATE)))\n",
    "\n",
    "model.add(Dense(QA_EMBED_SIZE))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型可视化 https://keras-cn.readthedocs.io/en/latest/other/visualization/\n",
    "from keras.utils import plot_model\n",
    "from IPython import display\n",
    "\n",
    "# pip install pydot-ng\n",
    "# sudo apt-get install graphviz\n",
    "plot_model(model, to_file=\"img/model-blstm.png\", show_shapes=True)\n",
    "display.Image('img/model-blstm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 57691 samples, validate on 24725 samples\n",
      "Epoch 1/5\n",
      "57691/57691 [==============================] - 4417s 77ms/step - loss: 0.5494 - acc: 0.7508 - precision: 0.7640 - recall: 0.9307 - f1: 0.8354 - val_loss: 0.4929 - val_acc: 0.7848 - val_precision: 0.7744 - val_recall: 0.9765 - val_f1: 0.8626\n",
      "Epoch 2/5\n",
      "57691/57691 [==============================] - 4365s 76ms/step - loss: 0.4895 - acc: 0.7919 - precision: 0.7740 - recall: 0.9914 - f1: 0.8681 - val_loss: 0.4726 - val_acc: 0.7975 - val_precision: 0.7756 - val_recall: 0.9992 - val_f1: 0.8722\n",
      "Epoch 3/5\n",
      "57691/57691 [==============================] - 4479s 78ms/step - loss: 0.4539 - acc: 0.8100 - precision: 0.7922 - recall: 0.9873 - f1: 0.8778 - val_loss: 0.4556 - val_acc: 0.8112 - val_precision: 0.7891 - val_recall: 0.9957 - val_f1: 0.8795\n",
      "Epoch 4/5\n",
      "57691/57691 [==============================] - 4437s 77ms/step - loss: 0.4274 - acc: 0.8226 - precision: 0.8025 - recall: 0.9898 - f1: 0.8852 - val_loss: 0.4085 - val_acc: 0.8301 - val_precision: 0.8051 - val_recall: 0.9986 - val_f1: 0.8905\n",
      "Epoch 5/5\n",
      "57691/57691 [==============================] - 4474s 78ms/step - loss: 0.4133 - acc: 0.8294 - precision: 0.8067 - recall: 0.9937 - f1: 0.8894 - val_loss: 0.4047 - val_acc: 0.8317 - val_precision: 0.8062 - val_recall: 0.9994 - val_f1: 0.8915\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fdea80accf8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "# from keras.utils import multi_gpu_model\n",
    "from evaluate import *\n",
    "\n",
    "EPOCHS = 5\n",
    "BATCH_SIZE = 64 * G\n",
    "VALIDATION_SPLIT = 0.3 # 30% 验证集\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "model_checkpoint = ModelCheckpoint('model/model-blstm.h5', save_best_only=True, save_weights_only=True)\n",
    "tensor_board = TensorBoard('log/tflog-blstm', write_graph=True, write_images=True)\n",
    "\n",
    "# model = multi_gpu_model(model)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy', precision, recall, f1])\n",
    "\n",
    "model.fit(X_train, Y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n",
    "          validation_split=VALIDATION_SPLIT, shuffle=True, \n",
    "          callbacks=[early_stopping, model_checkpoint, tensor_board])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20604/20604 [==============================] - 292s 14ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.40373429980029923,\n",
       " 0.8329935935001377,\n",
       " 0.8081239324661164,\n",
       " 0.9992693142158687,\n",
       " 0.892553141824807]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test, verbose=1, batch_size=BATCH_SIZE)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
